Po‐Thur Eve General‐07: Dosimetry of Small Lung Lesions with EGSnrc Monte Carlo and Treatment Planning Systems
Bibliographic record
Abstract
Early stage lung cancer, presenting as a small solitary pulmonary mass, is treated with radiation when concurrent disease precludes a surgical option. These small lesions are usually surrounded by less dense normal lung, which affects the ability to deliver a homogenous dose to the target volume. In low‐density tissue, such as lung, there is increased transmission of photons. In addition, lateral scatter of electrons out of the beam can lead to increased penumbral width. The magnitude of these effects is known to be dependent on beam energy. Some of the commonly used commercial treatment planning systems have had limited success in predicting accurately dose distributions under these highly inhomogeneous conditions. We present a quantitative comparison between Monte Carlo simulation and commercial planning systems for a select range of clinically relevant target geometries and beam parameters. Small water equivalent cylindrical lung tumors of diameter 3 and 5 cm were incorporated within a CT dataset at different locations. A Parallel Opposed Pair (POP) field arrangement with 6MV or 15MV photons and variable field‐edge margins were considered. These plans were calculated using BEAMnrc Monte Carlo code and on two planning systems; ADAC Pinnacle III Version 7.4 and MDS Nordion Theraplan Plus v3.8. The analysis of dose profiles and DVH's show considerable and unique differences between Monte Carlo and the results from each TPS within the tumor and at the junction between tumor and lung. For both planning systems, the severity of these errors, increases with photon energy, and decreases with field size.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".